ReflectanceGAN: Geospatial SAR-to-MSI Translation for Cloud-Agnostic Sentinel-2 Analytics

Optical multispectral imagery (MSI) is central to remote sensing and geospatial AI but suffers from cloud and revisit gaps, while C-band SAR is all-weather yet measures backscatter rather than surface reflectance. We present ReflectanceGAN, a reflectance-aware SAR → MSI translator that combines an SRGAN-style backbone with a shared encoder, a shallow cross-band mixer, and lightweight bandaware decoders for Blue/Green/Red/NIR/SWIR. Training is physics-informed via pixel and structural terms, a spectralangle objective to curb cross-band drift, and a differentiable index loss that preserves NDVI/NDWI/NDMI relationships under hinge cGAN supervision. Inputs are prepared with Refined Lee despeckling and polarization/texture features to stabilize learning while retaining edges. Evaluation emphasizes decision usefulness as well as fidelity: frozen classifiers and segmenters operate on real optics, generated optics, or SAR alone, and we report SSIM/PSNR/MSE alongside spectrumaware (SAM, ERGAS) and perceptual (LPIPS, FID) measures, plus index-consistency statistics. Robustness to region/season shifts and S1-S2 pairing gaps and interpretability analyses relating errors to SAR texture/geometry clarify when spectra are hardest to recover. ReflectanceGAN offers a compact, extensible pathway to cloud-agnostic MSI suitable for operational Earth observation.